As a novice python programmer, I love to make little programs to annoy my friends. (They love it too. Adobe cs6 all products activator (x32 y x64)-mpt exe. They won't admit it.) I noticed online, that python is only used for useful things, like web apps.
There really isn't a good prank python program tutorial out there. Luckily, I am here to change that. Today I will show you how to make a python calculator that gives bad answers! This tutorial assumes that you have some basic python knowledge, and that you have the python idle on your computer. (I used python 2.7).
Next we need to get the user's numbers, and operation. To do that we'll use raw input. However, rawinput makes whatever the user inputs a string. We don't want that. So make that string an integer by using int. We'll do this twice, to find the two numbers the user has in mind. Virtual serial ports emulator x64 cracked.
Retirement Calculator Python Programming
Here is the user number variables; num1 = int(rawinput('What is the first number')) num2 = int(rawinput('What is the first number')) Now we need to figure out what operation the user is doing. Use rawinput to ask. Op = rawinput('Are you adding, subtracting, multiplying or dividing? (input as +, -,., /)') Now the fun part, the random number. To do that, we need to make a variable to generate a random number, as an integer.
Here is how; rand = int(random.randint(2, 9)).
Post Outline:. What is Shortfall Risk (Shortfall Probability, Ruin Risk, Risk of Ruin etc.)?.
Issues with Traditional Shortfall Risk Estimates. Key Insights (Solutions). Python Code (Implementation) What is Shortfall Risk (Shortfall Probability, Risk of Ruin, etc.)? 'Shortfall risk is the risk that portfolio value will fall below some minimum acceptable level over some time horizon' - Issues with Traditional Shortfall Risk Estimates In the article ' the author, takes an insightful stance regarding estimates of retirement portfolio longevity. His approach addresses some key problems traditional, portfolio ruin risk estimation methods have, including. No one can definitively say what a 'good' shortfall probability is (is 10% good or bad?, 5%, 1%, 0.01%.N%?).
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Should a portfolio with an estimated 9% shortfall probability dominate a portfolio with a 10% shortfall probability? Is the theoretical 1% reduction in shortfall risk economically significant and/or accurate?. Portfolios with the same shortfall probability can have wide variation in their distributions of possible outcomes. Which 10% risk of ruin is better?. There is not enough market history to sample from.
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A retiree might have 30 years of retirement. Reliable asset class data extends back approximately 167 years.
Variables:. w = annual cash withdrawal in dollar units (ex. $50,000). M = current total 'nest egg' value measured in dollars (ex.
$1,000,000). g = annual growth rate of portfolio after all fees and taxes measured in percent (ex. 2%) Python Code (Implementation) To create a useful piece of code we need functions to process the following inputs:. N years of remaining life (integer). A range or array of possible growth rates in decimal form. Estimated annual cash spend/drawdown (integer/float). Estimated uncertainty of annual cash spend (decimal percent).
An array of future annual cash spend estimates. Current value of nest egg (float/integer) The first two helper functions I define below are roundPartial, and estimatelongevity. The roundPartial function takes a float value and a resolution value between (0,1) and outputs a rounded decimal to the percentage resolution given. The estimatelongevity function takes the variables (w, M, g) and outputs the estimated portfolio longevity in years rounded to the nearest quarter. Import pandas as pd import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl from Money import Money # for currency string formatting # # def roundPartial (value, resolution): return round (value / resolution). resolution # # def estimatelongevity(w, M, g): anlwpct = (w/M) # withdrawal rate as a percentage EL = 1/g.
np.log( anlwpct/(anlwpct - g) ) EL = roundPartial(EL,.25) return EL. This next function is a fun one. It creates a sensitivity table based on our nest egg value, estimated annual cash withdrawals, and estimated growth rates. I also define a helper function to easily create the money string format I use for the column labels. Note that in creating the dataframe the function iterates through both numerical and string arrays (lists) simultaneously using the zip function in order to maintain the index (str) and calculate the estimated longevity values (floats/integers). With the hard work complete I define several convenient plotting functions to summarize our output. First is a simple line plot function that takes our nest egg value and shows how our portfolio longevity estimates vary with our return estimate (g) and our annual cash withdrawal rates.
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It also plots a horizontal line to display the estimated personal lifespan remaining. Itools for mac os x. The second plot function takes our sensitivity table, and estimated personal lifespan and plots the estimated longevity of the portfolio less the personal lifespan remaining. In this way it plots the gaps between a portfolio's estimated longevity and an estimated lifespan in number of years. The final function takes the sensitivity table, and estimated lifespan and plots the same information as the second function except as a vertical barplot where the horizontal 'length' of the bars represents the number of years the portfolio is expected to last above or below the expected lifespan of the individual. # # def plotsensitivitytable(nestegg): fig = plt.figure mpl.rcParams'font.family' = 'CamingoCode' = tbl.plot(figsize=size, lw=2, marker='o') plt.axhline(y=estimatedpersonallongevity, color='k', ls='-', lw=2) plt.title('Estimated Portfolio Longevity Sensitivity nPortfolio Nest Egg =%s'% createmoneystr(nestegg), fontsize=17, fontweight='demi') plt.xlabel('Real Annual Growth Rate $(G)$') plt.ylabel('Estimated Portfolio Longevity $(Years)$') plt.legend(title='Real After-Tax Anl.
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